DAIMSL

DAIMSL focuses on safely and effectively operating robots in self-driving laboratories, utilizing immersive environments and digital twin (DT) technologies. The goal is to develop general interfaces for applying immersive and DT technologies across various laboratory processes, addressing a challenge not yet fully met by current bespoke systems.

The key objectives include establishing best practices for integrating augmented and virtual reality (A/VR) with robotic systems and their DTs, tackling issues such as resolution, representation, latency, safety, and usability. This will enhance our understanding of A/VR capabilities and limitations and lead to advancing the implementation of DT technologies in scientific robotics, and providing critical insights for educational and research applications.

A Digital Twin serves as a dynamic virtual model of a physical system, enabling a bidirectional data flow that supports simulations and real-time adjustments based on predictive analytics and user inputs. The project also focuses on designing and evaluating methods for DT data handling, which are crucial for optimizing laboratory processes and identifying potential risks.

AI model training involves data collected from human-operated lab tasks, with an emphasis on using transfer-learning to efficiently adapt pre-trained models to new tasks. Initial experiments are conducted in Argonne’s rapid prototyping lab, using an A/VR interface for simple chemistry tasks to ensure adherence to critical safety and experimental protocols.

As DOE laboratories increasingly adopt AI and robotics, this research is essential for enhancing the safety, quality, and efficiency of laboratory operations through advanced A/VR and DT technologies. The project delivers vital tools, methods, and insights for the future of automated and self-driving labs.